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Function of two redox sensing kinases from the methanogenic archaeon Methanosarcina acetivorans
(2019)
MsmS is a heme-based redox sensor kinase in Methanosarcina acetivorans consisting of alternating PAS and GAF domains connected to a C-terminal kinase domain. In addition to MsmS, M. acetivorans possesses a second kinase, MA0863 with high sequence similarity. Interestingly, MA0863 possesses an amber codon in its second GAF domain, encoding for the amino acid pyrrolysine. Thus far, no function of this residue has been resolved. In order to examine the heme iron coordination in both proteins, an improved method for the production of heme proteins was established using the Escherichia coli strain Nissle 1917. This method enables the complete reconstitution of a recombinant hemoprotein during protein production, thereby resulting in a native heme coordination. Analysis of the full-length MsmS and MA0863 confirmed a covalently bound heme cofactor, which is connected to one conserved cysteine residue in each protein. In order to identify the coordinating amino acid residues of the heme iron, UV/vis spectra of different variants were measured. These studies revealed His702 in MsmS and the corresponding His666 in MA0863 as the proximal heme ligands. MsmS has previously been described as a heme-based redox sensor. In order to examine whether the same is true for MA0863, redox dependent kinase assays were performed. MA0863 indeed displays redox dependent autophosphorylation activity, which is independent of heme ligands and only observed under oxidizing conditions. Interestingly, autophosphorylation was shown to be independent of the heme cofactor but rather relies on thiol oxidation. Therefore, MA0863 was renamed in RdmS (redox dependent methyltransferase-associated sensor). In order to identify the phosphorylation site of RdmS, thin layer chromatography was performed identifying a tyrosine as the putative phosphorylation site. This observation is in agreement with the lack of a so-called H-box in typical histidine kinases. Due to their genomic localization, MsmS and RdmS were postulated to form two-component systems (TCS) with vicinal encoded regulator proteins MsrG and MsrF. Therefore, protein-protein interaction studies using the bacterial adenylate two hybrid system were performed suggesting an interaction of RdmS and MsmS with the three regulators MsrG/F/C. Due to these multiple interactions these signal transduction pathways should rather be considered multicomponent system instead of two component systems.
Ranking lists are an essential methodology to succinctly summarize outstanding items, computed over database tables or crowdsourced in dedicated websites. In this thesis, we propose the usage of automatically generated, entity-centric rankings to discover insights in data. We present PALEO, a framework for data exploration through reverse engineering top-k database queries, that is, given a database and a sample top-k input list, our approach, aims at determining an SQL query that returns results similar to the provided input when executed over the database. The core problem consist of finding selection predicates that return the given items, determining the correct ranking criteria, and evaluating the most promising candidate queries first. PALEO operates on subset of the base data, uses data samples, histograms, descriptive statistics, and further proposes models that assess the suitability of candidate queries which facilitate limitation of false positives. Furthermore, this thesis presents COMPETE, a novel approach that models and computes dominance over user-provided input entities, given a database of top-k rankings. The resulting entities are found superior or inferior with tunable degree of dominance over the input set---a very intuitive, yet insightful way to explore pros and cons of entities of interest. Several notions of dominance are defined which differ in computational complexity and strictness of the dominance concept---yet, interdependent through containment relations. COMPETE is able to pick the most promising approach to satisfy a user request at minimal runtime latency, using a probabilistic model that is estimating the result sizes. The individual flavors of dominance are cast into a stack of algorithms over inverted indices and auxiliary structures, enabling pruning techniques to avoid significant data access over large datasets of rankings.
Wine and alcoholic fermentations are complex and fascinating ecosystems. Wine aroma is shaped by the wine’s chemical compositions, in which both microbes and grape constituents play crucial roles. Activities of the microbial community impact the sensory properties of the final product, therefore, the characterisation of microbial diversity is essential in understanding and predicting sensory properties of wine. Characterisation has been challenging with traditional approaches, where microbes are isolated and therefore analyzed outside from their natural environment. This causes a bias in the observed microbial composition structure. In addition, true community interactions cannot be studied using isolates. Furthermore, the multiplex ties between wine chemical and sensory compositions remain evasive due to their multivariate and nonlinear nature. Therefore, the sensorial outcome arising from different microbial communities has remained inconclusive.
In this thesis, microbial diversity during Riesling wine fermentations is investigated with the aim to understand the roles of microbial communities during fermentations and their links to sensory properties. With the advancement of high-throughput tools based ‘omic methods, such as next-generation sequencing (NGS) technologies, it is now possible to study microbial communities and their functions without isolation by culturing. This developing field and its potential to wine community is reviewed in Chapter 1. The standardisation of methods remains challenging in the field. DNA extraction is a key step in capturing the microbial diversity in samples for generating NGS data, therefore, DNA extraction methods are evaluated in Chapter 2. In Chapter 3, machine learning is utilized in guiding raw data mining generated by the untargeted GC-MS analysis. This step is crucial in order to take full advantages of the large scope of data generated by ‘omic methods. These lay a solid foundation for Chapters 4 and 5 where microbial community structures and their outputs - chemical and sensory compositions are studied by using approaches and tools based on multiple ‘omics methods.
The results of this thesis show first that by using novel statistical approaches, it is possible to extract meaningful information from heterogeneous biological, chemical and sensorial data. Secondly, results suggest that the variation in wine aroma, might be related
to microbial interactions taking place not only inside a single community, but also the
IV
interactions between communities, such as vineyard and winery communities. Therefore, the true sensory expression of terroir might be masked by the interaction between two microbial communities, although more work is needed to uncover this potential relationship. Such potential interaction mechanisms were uncovered between non- Saccharomyces yeast and bacteria in this work and unexpected novel bacterial growth was observed during alcohol fermentation. This suggests new layers in understanding of wine fermentations. In the future, multi-omic approaches could be applied to identify biological pathways leading to specific wine aroma as well as investigate the effects upon specific winemaking conditions. These results are relevant not just for the wine industry, but also to other industries where complex microbial networks are important. As such, the approaches presented in this thesis might find widely use in the food industry.
Large-scale distributed systems consist of a number of components, take a number of parameter values as input, and behave differently based on a number of non-deterministic events. All these features—components, parameter values, and events—interact in complicated ways, and unanticipated interactions may lead to bugs. Empirically, many bugs in these systems are caused by interactions of only a small number of features. In certain cases, it may be possible to test all interactions of \(k\) features for a small constant \(k\) by executing a family of tests that is exponentially or even doubly-exponentially smaller than the family of all tests. Thus, in such cases we can effectively uncover all bugs that require up to \(k\)-wise interactions of features.
In this thesis we study two occurrences of this phenomenon. First, many bugs in distributed systems are caused by network partition faults. In most cases these bugs occur due to two or three key nodes, such as leaders or replicas, not being able to communicate, or because the leading node finds itself in a block of the partition without quorum. Second, bugs may occur due to unexpected schedules (interleavings) of concurrent events—concurrent exchange of messages and concurrent access to shared resources. Again, many bugs depend only on the relative ordering of a small number of events. We call the smallest number of events whose ordering causes a bug the depth of the bug. We show that in both testing scenarios we can effectively uncover bugs involving small number of nodes or bugs of small depth by executing small families of tests.
We phrase both testing scenarios in terms of an abstract framework of tests, testing goals, and goal coverage. Sets of tests that cover all testing goals are called covering families. We give a general construction that shows that whenever a random test covers a fixed goal with sufficiently high probability, a small randomly chosen set of tests is a covering family with high probability. We then introduce concrete coverage notions relating to network partition faults and bugs of small depth. In case of network partition faults, we show that for the introduced coverage notions we can find a lower bound on the probability that a random test covers a given goal. Our general construction then yields a randomized testing procedure that achieves full coverage—and hence, find bugs—quickly.
In case of coverage notions related to bugs of small depth, if the events in the program form a non-trivial partial order, our general construction may give a suboptimal bound. Thus, we study other ways of constructing covering families. We show that if the events in a concurrent program are partially ordered as a tree, we can explicitly construct a covering family of small size: for balanced trees, our construction is polylogarithmic in the number of events. For the case when the partial order of events does not have a "nice" structure, and the events and their relation to previous events are revealed while the program is running, we give an online construction of covering families. Based on the construction, we develop a randomized scheduler called PCTCP that uniformly samples schedules from a covering family and has a rigorous guarantee of finding bugs of small depth. We experiment with an implementation of PCTCP on two real-world distributed systems—Zookeeper and Cassandra—and show that it can effectively find bugs.
Hardware Contention-Aware Real-Time Scheduling on Multi-Core Platforms in Safety-Critical Systems
(2019)
While the computing industry has shifted from single-core to multi-core processors for performance gain, safety-critical systems (SCSs) still require solutions that enable their transition while guaranteeing safety, requiring no source-code modifications and substantially reducing re-development and re-certification costs, especially for legacy applications that are typically substantial. This dissertation considers the problem of worst-case execution time (WCET) analysis under contentions when deadline-constrained tasks in independent partitioned task set execute on a homogeneous multi-core processor with dynamic time-triggered shared memory bandwidth partitioning in SCSs.
Memory bandwidth in multi-core processors is shared across cores and is a significant cause of performance bottleneck and temporal variability of multiple-orders in task’s execution times due to contentions in memory sub-system. Further, the circular dependency is not only between WCET and CPU scheduling of others cores, but also between WCET and memory bandwidth assignments over time to cores. Thus, there is need of solutions that allow tailoring memory bandwidth assignments to workloads over time and computing safe WCET. It is pragmatically infeasible to obtain WCET estimates from static WCET analysis tools for multi-core processors due to the sheer computational complexity involved.
We use synchronized periodic memory servers on all cores that regulate each core’s maximum memory bandwidth based on allocated bandwidth over time. First, we present a workload schedulability test for known even-memory-bandwidth-assignment-to-active-cores over time, where the number of active cores represents the cores with non-zero memory bandwidth assignment. Its computational complexity is similar to merge-sort. Second, we demonstrate using a real avionics certified safety-critical application how our method’s use can preserve an existing application’s single-core CPU schedule under contentions on a multi-core processor. It enables incremental certification using composability and requires no-source code modification.
Next, we provide a general framework to perform WCET analysis under dynamic memory bandwidth partitioning when changes in memory bandwidth to cores assignment are time-triggered and known. It provides a stall maximization algorithm that has a complexity similar to a concave optimization problem and efficiently implements the WCET analysis. Last, we demonstrate dynamic memory assignments and WCET analysis using our method significantly improves schedulability compared to the stateof-the-art using an Integrated Modular Avionics scenario.
This thesis addresses several challenges for sustainable logistics operations and investigates (1) the integration of intermediate stops in the route planning of transportation vehicles, which especially becomes relevant when alternative-fuel vehicles with limited driving range or a sparse refueling infrastructure are considered, (2) the combined planning of the battery replacement infrastructure and of the routing for battery electric vehicles, (3) the use of mobile load replenishment or refueling possibilities in environments where the respective infrastructure is not available, and (4) the additional consideration of the flow of goods from the end user in backward direction to the point of origin for the purpose of, e.g., recapturing value or proper disposal. We utilize models and solution methods from the domain of operations research to gain insights into the investigated problems and thus to support managerial decisions with respect to these issues.
Magnetoelastic coupling describes the mutual dependence of the elastic and magnetic fields and can be observed in certain types of materials, among which are the so-called "magnetostrictive materials". They belong to the large class of "smart materials", which change their shape, dimensions or material properties under the influence of an external field. The mechanical strain or deformation a material experiences due to an externally applied magnetic field is referred to as magnetostriction; the reciprocal effect, i.e. the change of the magnetization of a body subjected to mechanical stress is called inverse magnetostriction. The coupling of mechanical and electromagnetic fields is particularly observed in "giant magnetostrictive materials", alloys of ferromagnetic materials that can exhibit several thousand times greater magnitudes of magnetostriction (measured as the ratio of the change in length of the material to its original length) than the common magnetostrictive materials. These materials have wide applications areas: They are used as variable-stiffness devices, as sensors and actuators in mechanical systems or as artificial muscles. Possible application fields also include robotics, vibration control, hydraulics and sonar systems.
Although the computational treatment of coupled problems has seen great advances over the last decade, the underlying problem structure is often not fully understood nor taken into account when using black box simulation codes. A thorough analysis of the properties of coupled systems is thus an important task.
The thesis focuses on the mathematical modeling and analysis of the coupling effects in magnetostrictive materials. Under the assumption of linear and reversible material behavior with no magnetic hysteresis effects, a coupled magnetoelastic problem is set up using two different approaches: the magnetic scalar potential and vector potential formulations. On the basis of a minimum energy principle, a system of partial differential equations is derived and analyzed for both approaches. While the scalar potential model involves only stationary elastic and magnetic fields, the model using the magnetic vector potential accounts for different settings such as the eddy current approximation or the full Maxwell system in the frequency domain.
The distinctive feature of this work is the analysis of the obtained coupled magnetoelastic problems with regard to their structure, strong and weak formulations, the corresponding function spaces and the existence and uniqueness of the solutions. We show that the model based on the magnetic scalar potential constitutes a coupled saddle point problem with a penalty term. The main focus in proving the unique solvability of this problem lies on the verification of an inf-sup condition in the continuous and discrete cases. Furthermore, we discuss the impact of the reformulation of the coupled constitutive equations on the structure of the coupled problem and show that in contrast to the scalar potential approach, the vector potential formulation yields a symmetric system of PDEs. The dependence of the problem structure on the chosen formulation of the constitutive equations arises from the distinction of the energy and coenergy terms in the Lagrangian of the system. While certain combinations of the elastic and magnetic variables lead to a coupled magnetoelastic energy function yielding a symmetric problem, the use of their dual variables results in a coupled coenergy function for which a mixed problem is obtained.
The presented models are supplemented with numerical simulations carried out with MATLAB for different examples including a 1D Euler-Bernoulli beam under magnetic influence and a 2D magnetostrictive plate in the state of plane stress. The simulations are based on material data of Terfenol-D, a giant magnetostrictive materials used in many industrial applications.
Novel image processing techniques have been in development for decades, but most
of these techniques are barely used in real world applications. This results in a gap
between image processing research and real-world applications; this thesis aims to
close this gap. In an initial study, the quantification, propagation, and communication
of uncertainty were determined to be key features in gaining acceptance for
new image processing techniques in applications.
This thesis presents a holistic approach based on a novel image processing pipeline,
capable of quantifying, propagating, and communicating image uncertainty. This
work provides an improved image data transformation paradigm, extending image
data using a flexible, high-dimensional uncertainty model. Based on this, a completely
redesigned image processing pipeline is presented. In this pipeline, each
step respects and preserves the underlying image uncertainty, allowing image uncertainty
quantification, image pre-processing, image segmentation, and geometry
extraction. This is communicated by utilizing meaningful visualization methodologies
throughout each computational step.
The presented methods are examined qualitatively by comparing to the Stateof-
the-Art, in addition to user evaluation in different domains. To show the applicability
of the presented approach to real world scenarios, this thesis demonstrates
domain-specific problems and the successful implementation of the presented techniques
in these domains.
The focus of this work is to provide and evaluate a novel method for multifield topology-based analysis and visualization. Through this concept, called Pareto sets, one is capable to identify critical regions in a multifield with arbitrary many individual fields. It uses ideas found in graph optimization to find common behavior and areas of divergence between multiple optimization objectives. The connections between the latter areas can be reduced into a graph structure allowing for an abstract visualization of the multifield to support data exploration and understanding.
The research question that is answered in this dissertation is about the general capability and expandability of the Pareto set concept in context of visualization and application. Furthermore, the study of its relations, drawbacks and advantages towards other topological-based approaches. This questions is answered in several steps, including consideration and comparison with related work, a thorough introduction of the Pareto set itself as well as a framework for efficient implementation and an attached discussion regarding limitations of the concept and their implications for run time, suitable data, and possible improvements.
Furthermore, this work considers possible simplification approaches like integrated single-field simplification methods but also using common structures identified through the Pareto set concept to smooth all individual fields at once. These considerations are especially important for real-world scenarios to visualize highly complex data by removing small local structures without destroying information about larger, global trends.
To further emphasize possible improvements and expandability of the Pareto set concept, the thesis studies a variety of different real world applications. For each scenario, this work shows how the definition and visualization of the Pareto set is used and improved for data exploration and analysis based on the scenarios.
In summary, this dissertation provides a complete and sound summary of the Pareto set concept as ground work for future application of multifield data analysis. The possible scenarios include those presented in the application section, but are found in a wide range of research and industrial areas relying on uncertainty analysis, time-varying data, and ensembles of data sets in general.
Cell migration is essential for embryogenesis, wound healing, immune surveillance, and
progression of diseases, such as cancer metastasis. For the migration to occur, cellular
structures such as actomyosin cables and cell-substrate adhesion clusters must interact.
As cell trajectories exhibit a random character, so must such interactions. Furthermore,
migration often occurs in a crowded environment, where the collision outcome is deter-
mined by altered regulation of the aforementioned structures. In this work, guided by a
few fundamental attributes of cell motility, we construct a minimal stochastic cell migration
model from ground-up. The resulting model couples a deterministic actomyosin contrac-
tility mechanism with stochastic cell-substrate adhesion kinetics, and yields a well-defined
piecewise deterministic process. The signaling pathways regulating the contractility and
adhesion are considered as well. The model is extended to include cell collectives. Numer-
ical simulations of single cell migration reproduce several experimentally observed results,
including anomalous diffusion, tactic migration, and contact guidance. The simulations
of colliding cells explain the observed outcomes in terms of contact induced modification
of contractility and adhesion dynamics. These explained outcomes include modulation
of collision response and group behavior in the presence of an external signal, as well as
invasive and dispersive migration. Moreover, from the single cell model we deduce a pop-
ulation scale formulation for the migration of non-interacting cells. In this formulation,
the relationships concerning actomyosin contractility and adhesion clusters are maintained.
Thus, we construct a multiscale description of cell migration, whereby single, collective,
and population scale formulations are deduced from the relationships on the subcellular
level in a mathematically consistent way.
Spatial regression models provide the opportunity to analyse spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarises the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a non-spatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive (SAR) and spatial error (SEM) specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) as well as the simple SLX provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect `spillover' effects, several - quite realistic - situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.
On the Effect of Nanofillers on the Environmental Stress Cracking Resistance of Glassy Polymers
(2019)
It is well known that reinforcing polymers with small amounts of nano-sized fillers is one of the most effective methods for simultaneously improving their mechanical and thermal properties. However, only a small number of studies have focused on environ-mental stress cracking (ESC), which is a major issue for premature failures of plastic products in service. Therefore, the contribution of this work focused on the influence of nano-SiO2 particles on the morphological, optical, mechanical, thermal, as well as envi-ronmental stress cracking properties of amorphous-based nanocomposites.
Polycarbonate (PC), polystyrene (PS) and poly(methyl methacrylate) (PMMA) nanocom-posites containing different amounts and sizes of nano-SiO2 particles were prepared using a twin-screw extruder followed by injection molding. Adding a small amount of nano-SiO2 caused a reduction in optical properties but improved the tensile, toughness, and thermal properties of the polymer nanocomposites. The significant enhancement in mechanical and thermal properties was attributed to the adequate level of dispersion and interfacial interaction of the SiO2 nanoparticles in the polymer matrix. This situation possibly increased the efficiency of stress transfer across the nanocomposite compo-nents. Moreover, the data revealed a clear dependency on the filler size. The polymer nanocomposites filled with smaller nanofillers exhibited an outstanding enhancement in both mechanical properties and transparency compared with nanocomposites filled with larger particles. The best compromise of strength, toughness, and thermal proper-ties was achieved in PC-based nanocomposites. Therefore, special attention to the influ-ence of nanofiller on the ESC resistance was given to PC.
The ESC resistance of the materials was investigated under static loading with and without the presence of stress-cracking agents. Interestingly, the incorporation of nano-SiO2 greatly enhanced the ESC resistance of PC in all investigated fluids. This result was particularly evident with the smaller quantities and sizes of nano-SiO2. The enhancement in ESC resistance was more effective in mild agents and air, where the quality of the deformation process was vastly altered with the presence of nano-SiO2. This finding confirmed that the new structural arrangements on the molecular scale in-duced by nanoparticles dominate over the ESC agent absorption effect and result in greatly improving the ESC resistance of the materials. This effect was more pronounced with increasing molecular weight of PC due to an increase in craze stability and fibril density. The most important and new finding is that the ESC behavior of polymer-based nanocomposites/ stress-cracking agent combinations can be scaled using the Hansen solubility parameter. Thus allowed us to predict the risk of ESC as a function of the filler content for different stress-cracking agents without performing extensive tests. For a comparison of different amorphous polymer-based nanocomposites at a given nano-SiO2 particle content, the ESC resistance of materials improved in the following order: PMMA/SiO2 < PS/SiO2 < low molecular weight PC/SiO2 < high molecular weight PC/SiO2. In most cases, nanocomposites with 1 vol.% of nano-SiO2 particles exhibited the largest improvement in ESC resistance.
However, the remarkable improvement in the ESC resistance—particularly in PC-based nanocomposites—created some challenges related to material characterization because testing times (failure time) significantly increased. Accordingly, the superposition ap-proach has been applied to construct a master curve of crack propagation model from the available short-term tests at different temperatures. Good agreement of the master curves with the experimental data revealed that the superposition approach is a suitable comparative method for predicting slow crack growth behavior, particularly for long-duration cracking tests as in mild agents. This methodology made it possible to mini-mize testing time.
Additionally, modeling and simulations using the finite element method revealed that multi-field modeling could provide reasonable predictions for diffusion processes and their impact on fracture behavior in different stress cracking agents. This finding sug-gests that the implemented model may be a useful tool for quick screening and mitigat-ing the risk of ESC failures in plastic products.
Model uncertainty is a challenge that is inherent in many applications of mathematical models in various areas, for instance in mathematical finance and stochastic control. Optimization procedures in general take place under a particular model. This model, however, might be misspecified due to statistical estimation errors and incomplete information. In that sense, any specified model must be understood as an approximation of the unknown "true" model. Difficulties arise since a strategy which is optimal under the approximating model might perform rather bad in the true model. A natural way to deal with model uncertainty is to consider worst-case optimization.
The optimization problems that we are interested in are utility maximization problems in continuous-time financial markets. It is well known that drift parameters in such markets are notoriously difficult to estimate. To obtain strategies that are robust with respect to a possible misspecification of the drift we consider a worst-case utility maximization problem with ellipsoidal uncertainty sets for the drift parameter and with a constraint on the strategies that prevents a pure bond investment.
By a dual approach we derive an explicit representation of the optimal strategy and prove a minimax theorem. This enables us to show that the optimal strategy converges to a generalized uniform diversification strategy as uncertainty increases.
To come up with a reasonable uncertainty set, investors can use filtering techniques to estimate the drift of asset returns based on return observations as well as external sources of information, so-called expert opinions. In a Black-Scholes type financial market with a Gaussian drift process we investigate the asymptotic behavior of the filter as the frequency of expert opinions tends to infinity. We derive limit theorems stating that the information obtained from observing the discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process which can be interpreted as a continuous-time expert. Our convergence results carry over to convergence of the value function in a portfolio optimization problem with logarithmic utility.
Lastly, we use our observations about how expert opinions improve drift estimates for our robust utility maximization problem. We show that our duality approach carries over to a financial market with non-constant drift and time-dependence in the uncertainty set. A time-dependent uncertainty set can then be defined based on a generic filter. We apply this to various investor filtrations and investigate which effect expert opinions have on the robust strategies.
Private data analytics systems preferably provide required analytic accuracy to analysts and specified privacy to individuals whose data is analyzed. Devising a general system that works for a broad range of datasets and analytic scenarios has proven to be difficult.
Despite the advent of differentially private systems with proven formal privacy guarantees, industry still uses inferior ad-hoc mechanisms that provide better analytic accuracy. Differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their usability.
In my thesis I follow two approaches to improve the usability of private data analytics systems in general and differentially private systems in particular. First, I revisit ad-hoc mechanisms and explore the possibilities of systems that do not provide Differential Privacy or only a weak version thereof. Based on an attack analysis I devise a set of new protection mechanisms including Query Based Bookkeeping (QBB). In contrast to previous systems QBB only requires the history of analysts’ queries in order to provide privacy protection. In particular, QBB does not require knowledge about the protected individuals’ data.
In my second approach I use the insights gained with QBB to propose UniTraX, the first differentially private analytics system that allows to analyze part of a protected dataset without affecting the other parts and without giving up on accuracy. I show UniTraX’s usability by way of multiple case studies on real-world datasets across different domains. UniTraX allows more queries than previous differentially private data analytics systems at moderate runtime overheads.
Green Innovation Areas have been developed in the US context of urban development in order to jump-start innovative solutions in abandoned areas. Prospective types of uses in these areas are not predetermined, but should be experimental and innovative. So far they can comprise vast greenhouse uses to less extensive clover fields, but their potential is not yet fully discovered. Implementing new and innovative economic uses in urban areas is relatively new in research for urban areas, in particular, when development types like bioeconomy are implemented. The joint German–Mexican research presented in this article aims at exploring the use of vacant inner urban spaces as Green Innovation Areas—discussing their potentials for sustainable development of shrinking cities.
Functional Metallic Microcomponents via Liquid-Phase Multiphoton Direct Laser Writing: A Review
(2019)
We present an overview of functional metallic microstructures fabricated via direct laser writing out of the liquid phase. Metallic microstructures often are key components in diverse applications such as, e.g., microelectromechanical systems (MEMS). Since the metallic component’s functionality mostly depends on other components, a technology that enables on-chip fabrication of these metal structures is highly desirable. Direct laser writing via multiphoton absorption is such a fabrication method. In the past, it has mostly been used to fabricate multidimensional polymeric structures. However, during the last few years different groups have put effort into the development of novel photosensitive materials that enable fabrication of metallic—especially gold and silver—microstructures. The results of these efforts are summarized in this review and show that direct laser fabrication of metallic microstructures has reached the level of applicability.
Micronuclei-based model system reveals functional consequences of chromothripsis in human cells
(2019)
Cancer cells often harbor chromosomes in abnormal numbers and with aberrant structure. The consequences of these chromosomal aberrations are difficult to study in cancer, and therefore several model systems have been developed in recent years. We show that human cells with extra chromosome engineered via microcell-mediated chromosome transfer often gain massive chromosomal rearrangements. The rearrangements arose by chromosome shattering and rejoining as well as by replication-dependent mechanisms. We show that the isolated micronuclei lack functional lamin B1 and become prone to envelope rupture, which leads to DNA damage and aberrant replication. The presence of functional lamin B1 partly correlates with micronuclei size, suggesting that the proper assembly of nuclear envelope might be sensitive to membrane curvature. The chromosomal rearrangements in trisomic cells provide growth advantage compared to cells without rearrangements. Our model system enables to study mechanisms of massive chromosomal rearrangements of any chromosome and their consequences in human cells.
The usage of sensors in modern technical systems and consumer products is in a rapid increase. This advancement can be characterized by two major factors, namely, the mass introduction of consumer oriented sensing devices to the market and the sheer amount of sensor data being generated. These characteristics raise subsequent challenges regarding both the consumer sensing devices' reliability and the management and utilization of the generated sensor data. This thesis addresses these challenges through two main contributions. It presents a novel framework that leverages sentiment analysis techniques in order to assess the quality of consumer sensing devices. It also couples semantic technologies with big data technologies to present a new optimized approach for realization and management of semantic sensor data, hence providing a robust means of integration, analysis, and reuse of the generated data. The thesis also presents several applications that show the potential of the contributions in real-life scenarios.
Due to the broad range, growing feature set and fast release pace of new sensor-based products, evaluating these products is very challenging as standard product testing is not practical. As an alternative, an end-to-end aspect-based sentiment summarizer pipeline for evaluation of consumer sensing devices is presented. The pipeline uses product reviews to extract the sentiment at the aspect level and includes several components namely, product name extractor, aspects extractor and a lexicon-based sentiment extractor which handles multiple sentiment analysis challenges such as sentiment shifters, negations, and comparative sentences among others. The proposed summarizer's components generally outperform the state-of-the-art approaches. As a use case, features of the market leading fitness trackers are evaluated and a dynamic visual summarizer is presented to display the evaluation results and to provide personalized product recommendations for potential customers.
The increased usage of sensing devices in the consumer market is accompanied with increased deployment of sensors in various other fields such as industry, agriculture, and energy production systems. This necessitates using efficient and scalable methods for storing and processing of sensor data. Coupling big data technologies with semantic techniques not only helps to achieve the desired storage and processing goals, but also facilitates data integration, data analysis, and the utilization of data in unforeseen future applications through preserving the data generation context. This thesis proposes an efficient and scalable solution for semantification, storage and processing of raw sensor data through ontological modelling of sensor data and a novel encoding scheme that harnesses the split between the statements of the conceptual model of an ontology (TBox) and the individual facts (ABox) along with in-memory processing capabilities of modern big data systems. A sample use case is further introduced where a smartphone is deployed in a transportation bus to collect various sensor data which is then utilized in detecting street anomalies.
In addition to the aforementioned contributions, and to highlight the potential use cases of sensor data publicly available, a recommender system is developed using running route data, used for proximity-based retrieval, to provide personalized suggestions for new routes considering the runner's performance, visual and nature of route preferences.
This thesis aims at enhancing the integration of sensing devices in daily life applications through facilitating the public acquisition of consumer sensing devices. It also aims at achieving better integration and processing of sensor data in order to enable new potential usage scenarios of the raw generated data.
In recent years, the Federal Republic of Germany has been the most significant destination for asylum-related migration in the European Union. Asylum-related migrants are those who left their country of origin to search for better life in a country that is safe and usually provides better economic opportunities. Most of these migrants sought asylum; however, not all needed international protection. There is a continuum between forced and voluntary migration and the categorization and categories of migrants are very context-dependent. The main research questions in this research report are the following: 1. What kinds of asylum-related migrants (refugees, people with a temporary residence permit, asylum seekers, non-deportable former asylum seekers and undocumented migrants) live in Germany, in particular in Rhineland-Palatinate and Kaiserslautern? 2. What are the everyday lives of asylum-related migrants like in Rhineland-Palatinate and Kaiserslautern? 3. What are the migration wishes and plans of asylum-related migrants in Rhineland-Palatinate and Kaiserslautern? 4. How and for what reasons do asylum-related migrants in Rhineland-Palatinate and Kaiserslautern use the Internet and social media? The research questions are answered based on the empirical material collected during the field research in the spring and summer of 2019. In addition, earlier research and statistics on migrants, asylum seekers and refugees in Germany are utilized. Asylum-related migrants responded according to their own views; the results indicate both their perspectives and our interpretation of them.
A distributional solution framework is developed for systems consisting of linear hyperbolic partial differential equations (PDEs) and switched differential algebraic equations (DAEs) which are coupled via boundary conditions. The unique solvability is then characterize in terms of a switched delay DAE. The theory is illustrated with an example of electric power lines modeled by the telegraph equations which are coupled via a switching transformer where simulations confirm the predicted impulsive solutions.